Frontiers in big data · 2026
A prognostic tool for pulmonary collapse: nomogram-based prediction of 28-day mortality.
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He Xinming, Yu Wenchong, Li Yuling, Ma Ao, Meng Zhichao, Zhu Jiehao, Tan Minghui, Zhao Xiaodong, Chen Mu
Аннотация
BACKGROUND: Pulmonary collapse is a common and serious respiratory condition, but there is no dedicated bedside tool to estimate prognosis. This study aimed to develop a nomogram to predict 28-day mortality in patients with pulmonary collapse.
METHODS: We extracted data for patients with pulmonary collapse from MIMIC-III, identified predictors using regression analyses, and used MIMIC-IV for temporal validation. We then built a nomogram based on the selected predictors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), AUC comparisons using the DeLong test, reclassification (NRI and IDI), calibration (calibration curves, calibration slope, and Brier score), and decision curve analysis (DCA).
RESULTS: A total of 4,088 patients with pulmonary collapse were included in the study. Logistic regression analysis identified twelve independent predictive factors associated with 28-day mortality: age (OR = 1.01, P =0.040), married status (OR =0 .63, P =0 .040), Glasgow Coma Scale score (OR = 0.94, P = 0.02), creatinine (OR = 0.82, P = 0.03), chloride ions (OR = 0.85, P = 0.03), sodium ions (OR = 1.19, P = 0.02), blood urea nitrogen (OR = 1.02, P < 0.001), white blood cell count (OR = 1.04, P < 0.001), heart rate (OR = 1.02, P = 0.02), respiratory rate (OR = 1.05, P = 0.03), temperature (OR = 0.54, P < 0.001), and metastatic cancer (OR = 6.66, P < 0.001). The nomogram showed moderate discrimination and consistently higher AUC than Age+Gender, SOFA, and SAPSII across cohorts.
CONCLUSION: This study identified factors associated with 28-day mortality in patients with pulmonary collapse and developed a nomogram for early risk stratification.
METHODS: We extracted data for patients with pulmonary collapse from MIMIC-III, identified predictors using regression analyses, and used MIMIC-IV for temporal validation. We then built a nomogram based on the selected predictors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), AUC comparisons using the DeLong test, reclassification (NRI and IDI), calibration (calibration curves, calibration slope, and Brier score), and decision curve analysis (DCA).
RESULTS: A total of 4,088 patients with pulmonary collapse were included in the study. Logistic regression analysis identified twelve independent predictive factors associated with 28-day mortality: age (OR = 1.01, P =0.040), married status (OR =0 .63, P =0 .040), Glasgow Coma Scale score (OR = 0.94, P = 0.02), creatinine (OR = 0.82, P = 0.03), chloride ions (OR = 0.85, P = 0.03), sodium ions (OR = 1.19, P = 0.02), blood urea nitrogen (OR = 1.02, P < 0.001), white blood cell count (OR = 1.04, P < 0.001), heart rate (OR = 1.02, P = 0.02), respiratory rate (OR = 1.05, P = 0.03), temperature (OR = 0.54, P < 0.001), and metastatic cancer (OR = 6.66, P < 0.001). The nomogram showed moderate discrimination and consistently higher AUC than Age+Gender, SOFA, and SAPSII across cohorts.
CONCLUSION: This study identified factors associated with 28-day mortality in patients with pulmonary collapse and developed a nomogram for early risk stratification.